Lie detection method and system

ABSTRACT

The present disclosure provides a heart rate variability (HRV) analysis-based method of lie detection, related computer program product, computer readable storage medium and system. Also provided is a method of HRV analysis using strange entropy.

TECHNICAL FIELD

The present disclosure is in the general field of life science, and moreparticularly bioinformatics.

BACKGROUND

Heart rate variability (HRV) is a measure of the beat-to-beat variationsin heart beat and it may be regarded as an indicator of the activity ofautonomic regulation of circulatory function in response to an externalor internal stimulation.

An analysis of HRV may find use in lie detection, which is generallyperformed based on body reactions not easily controlled by consciousmind, including, but not limited to, heart rate and skin conductivity.While the subject is asked a series of questions, lying will typicallyproduce distinctive measurements of physiological responses of autonomicnervous system, due to emotional and psychological changes of thesubject during questioning.

SUMMARY

In one aspect, the present disclosure provides a lie detection method,which comprises receiving an input associated with HRV; and performingan HRV analysis based on the input associated with HRV to obtain a liedetection result; wherein said HRV analysis includes one or more ofnonlinear HRV analysis, or neural network-based linear HRV analysis.

In another aspect, the present disclosure provides a computer programproduct comprising one or more instructions recorded on amachine-readable recording medium for carrying out a method of liedetection as described in the present disclosure.

In another aspect, the present disclosure provides a computer readablestorage medium having a computer program encoded thereon, wherein saidcomputer program when executed by a computer instructs the computer toexecute a method of lie detection as described in the presentdisclosure.

In another aspect, the present disclosure provides a lie detectionsystem comprising a computing unit configured to receive an inputassociated with HRV; and perform an HRV analysis based on the inputassociated with HRV to obtain a lie detection result; wherein said HRVanalysis includes one or more of nonlinear HRV analysis, or neuralnetwork-based linear HRV analysis.

In another aspect, the present disclosure provides a method of HRVanalysis using strange entropy as described in the present disclosure.

In another aspect, the present disclosure provides a computer programproduct comprising one or more instructions recorded on amachine-readable recording medium for performing HRV analysis usingstrange entropy as described in the present disclosure.

In another aspect, the present disclosure provides a computer readablestorage medium having a computer program encoded thereon, wherein saidcomputer program when executed by a computer instructs the computer toperform HRV analysis using strange entropy as described in the presentdisclosure.

In another aspect, the present disclosure provides aHRV analysis systemcomprising a computing unit configured to perform HRV analysis usingstrange entropy as described in the present disclosure.

The foregoing is a summary and thus contains, by necessity,simplifications, generalization, and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, features, and advantages of the devices and/or processes and/orother subject matter described herein will become apparent in theteachings set forth herein. The summary is provided to introduce aselection of concepts in a simplified form that are further describedbelow in the Detailed Description. This summary is not intended toidentify key features or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in determining the scopeof the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features of the present disclosure will becomemore fully apparent from the following description and appended claims,taken in conjunction with the accompanying drawings. Understanding thatthese drawings depict only several embodiments in accordance with thedisclosure and are, therefore, not to be considered limiting of itsscope, the disclosure will be described with additional specificity anddetail through use of the accompanying drawings.

FIG. 1 is a flow chart showing an illustrative embodiment of a liedetection method described herein.

FIG. 2 shows illustrative embodiments of performing an HRV analysisdescribed herein.

FIG. 3 is a flow chart showing an illustrative embodiment of nonlinearHRV analysis using strange entropy.

FIG. 4 is a flow chart illustrating an embodiment of error backpropagation algorithm.

FIG. 5 is a schematic diagram illustrating an embodiment of a liedetection system described herein.

FIG. 6 shows illustrative m-word distribution resulting from nonlinearHRV analysis of a young healthy adult using strange entropy as describedherein.

FIG. 7 shows illustrative m-word distribution resulting from nonlinearHRV analysis of an old healthy adult using strange entropy as describedherein.

FIG. 8 shows illustrative m-word distribution resulting from nonlinearHRV analysis of a chronic heart failure (CHF) patient using strangeentropy as described herein.

FIG. 9 shows illustrative strange entropy values of young adults, senioradults and CHF patients.

FIG. 10 shows illustrative PSS values of young adults, senior adults andCHF patients.

FIG. 11 shows illustrative analysis result of a young adult under normalcondition using a strange entropy-based HRV analysis method describedherein.

FIG. 12 shows illustrative analysis result of the same young adult underanxious condition using a strange entropy-based HRV analysis methoddescribed herein.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe Figures, can be arranged, substituted, combined, and designed in awide variety of different configurations, all of which are explicitlycontemplated and make part of this disclosure.

This disclosure provides, inter alia, methods, computer programproducts, computer readable storage media and systems for lie detectionbased on HRV analysis and for HRV analysis using strange entropy.

In one aspect, the present disclosure provides a method of liedetection, which comprises receiving an input associated with HRV; andperforming an HRV analysis based on the input associated with HRV toobtain a lie detection result, wherein said HRV analysis includes one ormore of nonlinear HRV analysis, or neural network-based linear HRVanalysis.

FIG. 1 shows an operational flow 100 representing an illustrativeembodiment of the lie detection method provided in the presentdisclosure. As shown in FIG. 1, the methods include an input receivingoperation 110, that includes receiving an input associated with HRV; ananalysis operation 120, that includes performing an HRV analysis basedon the input associated with HRV to obtain a lie detection result; andan optional output operation 130, that includes outputting one or moreof the analysis results or lie detection results.

In FIG. 1 and in the following figures that include various illustrativeembodiments of operational flows, discussion and explanation may beprovided with respect to methods and apparatus described herein, and/orwith respect to other examples and contexts. The operational flows mayalso be executed in a variety of other contexts and environments, and/orin modified versions of those described herein. In addition, althoughsome of the operational flows are presented in sequence, the variousoperations may be performed in various repetitions, concurrently, and/orin other orders than those that are illustrated.

In the input receiving operation 110, an input associated with HRV isreceived. The input associated with HRV useful for lie detection asdescribed herein may be any measurements that are able to indicateand/or calculate HRV, including, without limitation, measurementsderived from ECG, arterial pressure tracking, and pulse wave signalmeasured by means of e.g. photoplethysmograph (PPG). In an illustrativeembodiment, the input associated with HRV includes a time series of ECGrecordings.

In some embodiments, the input associated with HRV is received at thesame site as where the analysis operation is executed. In otherembodiments, the input associated with HRV may be obtained remotely froma site where the analysis operation is executed. In illustrativeembodiments, the input associated with HRV of a subject is received andthen analyzed in a different place from where questioning andmeasurements take place. These measurements can be transmitted to thesite of analysis by any suitable wireless/wired communication methods,including, without limitation, GSM/GPRS network, Bluetooth, internet andany equivalent means.

In some embodiments, the input associated with HRV is received in areal-time manner. In other embodiments, the measurements may be storedin a database and retrieved later to generate the input.

From the input receiving operation 110, the operational flow 100 movesto the analysis operation 120, where a HRV analysis is performed basedon the input associated with HRV to obtain a lie detection result. SaidHRV analysis includes one or more of nonlinear HRV analysis, or neuralnetwork based-linear HRV analysis. In an illustrative embodiment, saidHRV analysis includes nonlinear HRV analysis. In another illustrativeembodiment, said HRV analysis includes neural network-based linear HRVanalysis. In yet another illustrative embodiment, said HRV analysisincludes neural network-based linear HRV analysis in combination withnonlinear HRV analysis, and the neural network analysis is performedbased on a combination of linear HRV parameters and nonlinear HRVparameters.

Various nonlinear analysis methods may be adapted to be used in theHRV-based lie detection method provided in the present disclosure.Illustrative examples of useful nonlinear analysis methods include, butare not limited to, strange entropy (StEn), Chaos, correlationdimension, fractal theory, strange attractors, mode entropy (modEn),multifractal, multiscale multifractal, Lyapunov index, base-scaleentropy, and approximate entropy (ApEn). In an illustrative embodiment,the nonlinear HRV analysis is performed using strange entropy method asdetailed below.

In some embodiments, performing an HRV analysis based on the inputassociated with HRV using neural network-based linear HRV analysisincludes acquiring one or more linear HRV parameters from the inputassociated with HRV; and performing a neural network analysis based onthe linear HRV parameters.

In some embodiments, performing an HRV analysis based on the inputassociated 30 with HRV using neural network-based linear HRV analysis incombination with nonlinear HRV analysis includes acquiring one or morelinear HRV parameters from the input associated with HRV; obtaining oneor more nonlinear HRV parameters from the input associated with HRV; andperforming a neural network analysis based on the one or more linear HRVparameters and the one or more nonlinear HRV parameters.

FIG. 2 illustrates optional embodiments of the analysis operation 120.The analysis operation 120, performing an HRV analysis based on theinput associated with HRV, may optionally include, but are not limitedto, operation 1210 representing illustrative embodiments of nonlinearHRV analysis, operations 1220 to 1230 representing illustrativeembodiments of neural network-based linear HRV analysis, and operations1240 to 1260 representing illustrative embodiments of neuralnetwork-based linear HRV analysis in combination with nonlinear HRVanalysis.

At the optional operation 1210, a nonlinear HRV analysis based on theinput associated with HRV may be performed.

In an illustrative embodiment, the nonlinear HRV analysis is performedby using strange entropy method.

In certain embodiments, said strange entropy method may include taking atime series signal with N elements; calculating the base scale BS(i) foreach vector X(i); calculating probability of S(i); and obtaining one ormore of nonlinear HRV parameters or m-word distribution graph accordingto the probability of S(i). Accordingly, the present disclosure providesa method of HRV analysis using strange entropy.

In some embodiments, said strange entropy method includes taking a timeseries signal with N elements, u:{u(i):1≦i≦N}, wherein u(i) representssignals carried in the input associated with HRV, and for each u(i),there is a corresponding vector with m elements X(i)=[u(i), u(i+1), . .. , u(i+m−1)]; calculating a base scale BS(i) for each vector X(i);transforming every X(i) to a m-dimensional symbol series S_(i)={s(i), .. . s(i+m−1)}, sεA(A=0, 1, 2, 3) based on a scale of a×BS(i);calculating the probability of S_(i), wherein the probability of eachdifferent combination among the entire N−m+1 m-dimensional vectors is

${{p(\pi)} = \frac{\#\mspace{14mu}\left\{ t \middle| {\left( {u_{i},\ldots\mspace{14mu},u_{t + m - 1}} \right)\mspace{14mu}{has}\mspace{14mu}{type}\mspace{14mu}\pi} \right\}}{N - m + 1}},$wherein 1≦t≦N−m+1; # is the number of states, and each possiblecombination π for S_(i) represents a vibration mode for S_(i); andobtaining one or more of nonlinear HRV parameters or m-word distributiongraph according to the probability of S_(i).

FIG. 3 shows an operational flow illustrating this embodiment, includingoperations 1211 to 1214.

At operation 1211, a time series signal with N elements may be taken,for instance, u:{u(i):1≦i≦N}, wherein u(i) represents signals carried inthe input associated with HRV. In illustrative embodiments, N needs tobe larger than 4^(m). In some embodiments, u(i) represents beat-to-beatinterval. In an illustrative embodiment, u(i) represents R-R interval,derived from e.g. ECG recordings. For each u(i), there is acorresponding vector with m elements X(i)=[u(i), u(+1), . . . ,u(i+m−1)], wherein m can be in the range of 2-6. In an illustrativeembodiment, m is 4.

At operation 1212, the base scale (BS) BS(i) for each vector X(i) may becalculated. BS(i) is defined as the square root average of differencebetween adjacent elements. In illustrative embodiments, BS(i) iscalculated according to the following equation

${{BS}(i)} = {\sqrt{\frac{\sum\limits_{j = 1}^{m - 1}\left( \left( {{u\left( {i + j} \right)} - {u\left( {i + j - 1} \right)}} \right)^{2} \right.}{m - 1}}.}$

Based on a scale of a×BS(i), every X(i) may be transformed to am-dimensional symbol series S_(i)={s(i), . . . s(i+m−1)}, sεA(A=0, 1, 2,3). In illustrative embodiments, the transformation equation is

$S_{i + k} = \left\{ \begin{matrix}{0\text{:}} & {{\overset{\_}{u}}_{i} < u_{i + k} \leq {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{1\text{:}} & {u_{i + k} > {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{2\text{:}} & {{{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}} < u_{i + k} \leq {\overset{\_}{u}}_{i}} \\{3\text{:}} & {u_{i + k} \leq {{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}}}\end{matrix} \right.$wherein i=1, 2, . . . , N−m+1; k=0, 1, . . . m−1; S_(i+k) is the k-thelement of S_(i); u_(i+k) is the k-th element of X(i); ū_(i) representsthe average value of the m-dimensional vector X(i); BS(i) represents thebase scale of X(i). 0, 1, 2, 3 are merely illustrative notation forregion partition and the value assignments do not stand for any actualmeanings. The value assignment of a cannot be too small to ignore noise,or too large, in case that detail information may be lost during thetransformation from original time series to symbol series and thusinadequate for capturing the dynamic information in the signal. Inillustrative embodiments, a can be in the range of 0.1-2. In certainembodiments, a can be in the range of 0.1-0.4. In an illustrativeembodiment, a is 0.2.

At operation 1213, the probability of S_(i) may be calculated.Accordingly the probability of each different combination among theentire N−m+1 m-dimensional vectors may be calculated. In illustrativeembodiments, the probability of S_(i) may be calculated using thefollowing equation

${p(\pi)} = \frac{\#\mspace{14mu}\left\{ t \middle| {\left( {u_{i},\ldots\mspace{14mu},u_{t + m - 1}} \right)\mspace{14mu}{has}\mspace{14mu}{type}\mspace{14mu}\pi} \right\}}{N - m + 1}$wherein 1≦t≦N−m+1, # is the number of states, and each possiblecombination π for S_(i) represents a vibration mode for S_(i). Thecombinations with the probability of S_(i) equaling to zero are noted asa forbidden state.

At operation 1214, one or more of nonlinear HRV parameters or m-worddistribution graph according to the probability of S_(i) may beobtained. In illustrative embodiments, nonlinear HRV parameters orm-word distribution graph may be obtained by defining the parameter H(m)(strange entropy, StEn) according to the following formulaH(m)=−ΣP(π)log₂ P(π).H(m) describes the information of m continuous values in time series. Inillustrative embodiments, if there are π states with the sameprobability, then H(m)=log₂4^(m); if there is only one possible state intime series, then H(m)=0. For situations in between, there is0<H(m)<log₂4^(m). High entropy indicates complexity in time series. Onthe other hand, low entropy indicates order in series.

Nonlinear HRV parameters or m-word distribution graph according to theprobability of S_(i) can also be obtained by defining the parameterprobability of strange states (PSS) used to indicate the sum ofprobabilities of all strange states, calculated according to thefollowing equation

${P\; S\; S} = \frac{\sum P_{s}}{\sum\limits_{i = 0}^{255}P_{i}}$wherein P_(i) is probability of combination i, P_(s) is probability ofstrange state, and ΣP_(s) is probability sum of all strange states.

PSS reflects the ratio of e.g. four healthy combinations among allpossible combinations. A larger PSS value means a more healthy vibrationmode.

After acquiring one or more of nonlinear HRV parameters, such as H(m)and PSS, or m-word distribution graph, by nonlinear HRV analysis, usingone or more of strange entropy or other methods, a conclusion of whethera person is lying or not may be drawn by interpreting those nonlinearparameters and/or graphs, both independently and in variouscombinations.

In certain embodiments, the lie detection result is obtained bycomparing analysis results obtained from a test subject with normaldata. The normal data may include, without limitation, data obtainedfrom the same test subject while he/she is in a normal state without anystimulation or disturbance, and data obtained from normal population ingeneral. Any identifiable difference between the data obtained from thetest subject and normal data suggests that the test subject may be underabnormal psychological conditions such as lying. In an illustrativeembodiment, the difference is significant.

In certain embodiments, the lie detection result is obtained byreal-time monitoring using a sliding window. The test subject may bemonitored for one or more of those nonlinear parameters or graphs duringa lie detection test in a real-time manner using a sliding window. As alie detection test usually takes a relatively long time, there will beenough number of data points to be analyzed. In illustrativeembodiments, the data points represent heart beats. The sliding windowwill typically include an appropriate number of data points. In certainembodiments, the window size could be in the range of 300-500 datapoints with the data points representing heart beats. In thisembodiment, the window size is equivalent to the number of data pointsobtained within around 2-6 minutes given a heart rate of about 80 ormore. In illustrative embodiments, the sliding step could be between 1and 50 data points.

In addition to nonlinear HRV analysis as described above, a liedetection result may also be obtained by performing neural network-basedlinear HRV analysis. In illustrative embodiments, the analysis operation120 may include optional operations 1220 to 1230, as shown in FIG. 2.

At the optional operation 1220, one or more linear HRV parameters may beacquired from the input associated with HRV.

The linear HRV parameters, including, without limitation,frequency-domain parameters, such as total power (TP), low frequency(LF) power, high frequency (HF) power, normalized LF (LFnorm),normalized HF (HFnorm), low frequency power/high frequency power(LF/HF), and time-domain parameters, such as heart rate (HR), andstandard deviation of adjacent R-R Interval (rMSSD), may be obtainedfrom linear HRV analysis and are interdependent.

Linear HRV analysis can be carried out in either or both offrequency-domain and time-domain.

Usually time-domain analysis is suitable for long-term analysis, forinstance, as long as 24 hours or even longer. In an illustrativeembodiment, HR analysis could be performed to monitor slow-changing partin HRV. In another illustrative embodiment, an rMSSD analysis could beperformed to monitor fast-changing part in HRV, which can be calculatedby using the following equation

${{rMSSD} = {\sqrt{\sum\limits_{i = 1}^{n - 1}{\left( {X_{i + 1} - X_{i}} \right)^{2}/\left( {n - 1} \right)}}\mspace{14mu}({ms})}},$wherein X stands for beat-to-beat interval, e.g. R-R interval.

Frequency-domain analysis is often used for short-term analysis, whichmay last 5 minutes for instance.

In illustrative embodiments, frequency-domain parameters may becalculated according to the definition of HRV spectrum segment.

In illustrative embodiments, the ultra low frequency (ULF) powerindicates a power at a frequency lower than 0.003 Hz, the very lowfrequency (VLF) power indicates a power at a frequency lower than 0.04Hz, the low frequency (LF) power indicates a power at a frequency withina range from 0.04 Hz to 0.15 Hz, and the high frequency (HF) indicates apower at a frequency within a range from 0.15 Hz to 0.4 Hz. Thecalculation of the total power (TP) is to calculate a sum of powers ofall frequency domain data corresponding to the heart rate data within acertain frequency range, e.g. a range under 0.4 Hz. In other words, theTP is obtained by adding the powers of all the frequency domain datacorresponding to the heart rate data. The calculation of the LF power isto calculate a sum of powers of all frequency domain data correspondingto the heart rate data within a range from 0.04 Hz to 0.15 Hz. Thecalculation of the HF power is to calculate a sum of powers of allfrequency domain data corresponding to the heart rate data within arange from 0.15 Hz to 0.4 Hz. The calculation of the VLF power is tocalculate a sum of powers of all frequency domain data corresponding tothe heart rate data within a range under 0.04 Hz. The calculation of theULF power is to calculate a sum of powers of all frequency domain datacorresponding to the heart rate data within a range under 0.003 Hz.

The parameters LFnorm, HFnorm and LF/HF may then be calculated based onthe parameters LF, HF and TP obtained in the calculation of thefrequency power as described above. The calculation of LF/HF is tocalculate the ratio of LF to HF. In an illustrative embodiment, thecalculation of LFnorm or HFnorm includes dividing the value of LF or HFby the value of the difference between the total power and the VLF, andmultiplying the division result by 100, thereby obtaining a normalizedLF or HF value.

At the optional operation 1230, a neural network analysis based on thelinear HRV parameters may be performed. In illustrative embodiments, theneural network analysis requires at least three linear HRV parameters.

The artificial neural network has a good self-study capability and acapacity of fitting any nonlinear functions. A well-trained neuralnetwork has already been self-adjusting during the training procedureand thus once an undetermined parameter value is input into the neuralnetwork, it can be determined rapidly whether it belongs to the statusof lying or non-lying.

Besides using a well-trained neural network, performing neural networkanalysis may also include training a neural network. In an illustrativeembodiment, the neural network is trained by an Error Back Propagation(BP) algorithm, so as to get a neural network with the ability todistinguish the physiological characteristics under lying condition fromthose under non-lying condition.

The error back propagation algorithm is a learning procedure of anartificial neural network, which may include a forward propagation ofthe signal and a back propagation of the error.

In an illustrative embodiment, the values of five parameters, e.g.LFnorm, HFnorm, LF/HF, TP, and rMSSD, of 16 persons under lyingcondition and another 16 persons under non-lying condition are provided.Half of those under lying condition are normal persons and the otherhalf are criminal persons. Those values may be used as the input samplesfor the neural network to make it trained, so that a more accurate fivedimension curved surface may be obtained.

FIG. 4 illustrates an embodiment of the error back propagation algorithmrepresenting an illustrative embodiment of neural network training usingthose values as the input samples for the neural network. As shown inFIG. 4, the operational flow 400 includes a parameters initializingoperation 410, that includes initializing the parameters for the inputsamples calculation; an output error obtaining operation 420, thatincludes obtaining the output error according to the received inputsamples; a error signals distributing operation 430, that includesdistributing the error signals of the layers and adjusting the weightsfor the calculation according to the distributed result; and a totalerror checking operation 440, that includes checking the total error ofthe network.

In the parameters initializing operation 410, the parameters for theinput samples calculation are initialized, for example, the weights. Theinitial values of the weights may be random numbers between 0.2 and 0.4.

In the output error obtaining operation 420, after the input samples arereceived and transmitted to be calculated, the output error is obtainedas the difference between the output value and the expected value.

Supposing the value of non-lying is used as an expected value, thestatus may be determined as lying when the value output by the outputlayer is greater than 0.5, and may be determined as non-lying when thevalue is lower than 0.5. Herein, the value 0.5 may be considered as themaximum error value between the output value and the expected value.That is, when the difference between the output value and the expectedvalue of non-lying does not exceed the maximum error value, it may beconsidered that the output value is in accord with the expected value ofnon-lying, and the status is non-lying. Similarly, when the differencebetween the output value and the expected value of non-lying exceeds themaximum error value, it is considered that the output value is not inaccord with the expected value of non-lying, and the status is lyinginstead of non-lying.

In the error signals distributing operation 430, the error signals ofthe layers are distributed, and the weights for the calculation areadjusted according to the distributed result.

If the output value from the output layer is not in accord with theexpected value, the output error is transmitted backward, so that theerror may be distributed over all the calculation, to obtain an errorsignal from each unit, thereby correcting the weights of the units.Generally a weight may be a random number between −1 to +1. Theprocedure of adjusting the weights is a procedure of learning andtraining of the neural network.

In the total error checking operation 440, it is checked whether thetotal error of the network meets the requirement of precision. If yes,the training is ended; otherwise, the processing returns to the outputerror obtaining operation 420. For example, supposing 0.5 is apredefined error value, if the total error of the network exceeds 0.5,it may be decided that the output value of the output layer is not inaccord with the expected value, and the processing returns to the outputerror obtaining operation 420.

In illustrative embodiments, when applying a neural network analysis,the parameters used as inputs of the neural network analysis may also bea combination of both linear HRV parameters and nonlinear HRVparameters. The methods to acquire linear HRV parameters and nonlinearHRV parameters can be, but are not limited to, any or all of the methodsmentioned above and description thereof is omitted herein.

As shown in FIG. 2, the analysis operation 120 may include optionaloperations 1240 to 1260 representing an illustrative embodiment of aneural network analysis based on a combination of linear HRV parametersand nonlinear HRV parameters.

At the optional operation 1240, one or more linear HRV parameters may beacquired from the input associated with HRV. At the optional operation1250, one or more nonlinear HRV parameters may be obtained from theinput associated with HRV. At the optional operation 1260, a neuralnetwork analysis based on the linear and nonlinear HRV parameters may beperformed.

In illustrative embodiments, combinations of linear HRV parameters andnonlinear HRV parameters may include two nonlinear parameters plus oneto six linear parameters. Those linear HRV parameters may include, butare not limited to, Total Power (TP), Low Frequency (LF) Power, HighFrequency (HF) Power, LFnorm, HFnorm, Low Frequency Power/High FrequencyPower (LF/HF), Heart Rate (HR), and Standard Deviation of adjacent R-RInterval (rMSSD), whereas nonlinear HRV parameters may include, but arenot limited to, H(m) and PSS. Some illustrative combinations may includeLFnorm, LF/HF, HR, H(m) and PSS; LF/HF, HR, rMSSD, H(m) and PSS; LFnorm,LF/HF, TP, H(m) and PSS; TP, LF/HF, HR, H(m) and PSS; and LF/HF, HFnorm,rMSSD, H(m) and PSS. The neural network analysis based on a combinationof linear HRV parameters and nonlinear HRV parameters may be performedas described as above.

In certain embodiments, a lie detection method described herein mayfurther comprise outputting one or more of the analysis results or liedetection results in any suitable form, including, without limitation,number values, graphs, and words. In some embodiments, output may be inthe form of number values, e.g. of nonlinear HRV parameters such as H(m)and PSS, or neural network analysis results. In some embodiments, outputmay be in the form of graph, including, without limitation, m-worddistribution graph. In some embodiments, output may be in the form ofsimple words such as “lying” and “non-lying”.

In certain embodiments, a lie detection method described herein mayfurther comprise taking a surveillance video and/or tape of the testsubject to facilitate interpretation of the analysis results. In thisregard, useful information extracted from the surveillance video or tapemay include, without limitation, body language, facial expression andchange in voice. As with the input associated with HRV, the surveillancevideo and/or tape may be analyzed on-site, may be stored in a databasefor later use, or may be transmitted to the analysis site different fromwhere questioning is held by any suitable wireless/wired communicationmethods, including, without limitation, GSM/GPRS network, Bluetooth,internet and any equivalent means.

In another aspect, the present disclosure provides a method of HRVanalysis using strange entropy, comprising taking a time series signalwith N elements, u:{u(i):1≦i≦1} wherein u(i) represents signals carriedin the input associated with HRV, and for each u(i), there is acorresponding vector with m elements X(i)=[u(i), u(i+1), . . . ,u(i+m−1)]; calculating a base scale BS(i) for each vector X(i);transforming every X(i) to a m-dimensional symbol series S_(i)={s(i), .. . s(i+m−1)}, sεA(A=0, 1, 2, 3) based on a scale of a×BS(i);calculating the probability of S_(i), wherein the probability of eachdifferent combination among the entire N−m+1 m-dimensional vectors is

${{p(\pi)} = \frac{\#\mspace{14mu}\left\{ t \middle| {\left( {u_{i},\ldots\mspace{14mu},u_{t + m - 1}} \right)\mspace{14mu}{has}\mspace{14mu}{type}\mspace{14mu}\pi} \right\}}{N - m + 1}},$wherein 1≦t≦N−m+1; # is the number of states, and each possiblecombination π for S_(i) represents a vibration mode for S_(i); andobtaining one or more of nonlinear HRV parameters or m-word distributiongraph according to the probability of S_(i).

In certain embodiments, u(i) represents R-R interval.

In certain embodiments, BS(i) is defined as square root average ofdifference between adjacent elements

${{BS}(i)} = {\sqrt{\frac{\sum\limits_{j = 1}^{m - 1}\left( \left( {{u\left( {i + j} \right)} - {u\left( {i + j - 1} \right)}} \right)^{2} \right.}{m - 1}}.}$

In certain embodiment, said every X(i) is transformed to a m-dimensionalsymbol series S_(i) according to the transformation equation

$S_{i + k} = \left\{ \begin{matrix}{0\text{:}} & {{\overset{\_}{u}}_{i} < u_{i + k} \leq {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{1\text{:}} & {u_{i + k} > {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{2\text{:}} & {{{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}} < u_{i + k} \leq {\overset{\_}{u}}_{i}} \\{3\text{:}} & {{u_{i + k} \leq {{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}}};}\end{matrix} \right.$wherein i=1, 2, . . . , N−m+1; k=0, 1, . . . m−1; S_(i+k) is the k-thelement of S_(i); u_(i+k) is the k-th element of X(i); ū_(i) representsthe average value of the m-dimensional vector X(i); and 0, 1, 2, 3 arenotation for region partition.

In certain embodiments, said nonlinear HRV parameters include H(m) whichis calculated according to the following equationH(m)=−ΣP(π)log₂ P(π).

In certain embodiments, said nonlinear HRV parameters include PSS whichis defined as sum of probabilities of all strange states calculatedaccording to following equation

${{P\; S\; S} = \frac{\sum P_{s}}{\sum\limits_{i = 0}^{255}P_{i}}};$wherein P_(i) is probability of combination i, P_(s) is probability ofstrange state, and ΣP_(s) is probability sum of all strange states.

In certain embodiments, m is in range of 2-6. In an illustrativeembodiment, m is 4.

In certain embodiments, a is in the range of 0.1-2. In certainembodiments, a is in the range of 0.1-0.4. In an illustrativeembodiment, a is 0.2.

In certain embodiments, N is larger than 4^(m).

Computer Programs Product, Computer Readable Storage Medium and Systems

In another aspect, the present disclosure provides a computer programproduct comprising one or more instructions recorded on amachine-readable recording medium for lie detection, wherein theinstructions include one or more instructions for receiving an inputassociated with HRV; and one or more instructions for performing an HRVanalysis based on the input associated with HRV to obtain a liedetection result; wherein said HRV analysis includes one or more ofnonlinear HRV analysis, or neural network-based linear HRV analysis.

In some embodiments of the computer program product described herein,the instructions may further comprise one or more instructions foroutputting one or more of the analysis results or lie detection results.

A person with ordinary skill in the art will appreciate that a computerprogram product described herein are capable of being distributed in avariety of forms via a signal bearing medium, and that the programproduct described herein applies regardless of the particular type ofsignal bearing medium used to actually carry out the distribution.Examples of a signal bearing medium include, but are not limited to, arecordable type medium such as a floppy disk, a hard disk drive, aCompact Disc (CD), a Digital Video Disk (DVD), a digital tape, acomputer memory, etc.; and a transmission type medium such as a digitaland/or an analog communication medium (e.g., a fiber optic cable, awaveguide, a wired communications link, a wireless communication link,etc.).

The machine-readable storage media mentioned above include, but notlimited to various memories and storage units, a semiconductor device, adisk unit such as an optic disk, a magnetic disk and a magneto-opticdisk, and other media applicable to information storage. It is alsopossible to execute the program on a client computer after computerprogram codes are downloaded and installed on the computer from acorresponding internet website connected with the computer.

In certain embodiments, said input associated with HRV includes a timeseries of ECG recordings.

In certain embodiments, the one or more instructions for performing anHRV analysis based on the input associated with HRV include one or moreinstructions for performing nonlinear HRV analysis based on the inputassociated with HRV.

In certain embodiments, the one or more instructions for performing nonlinear HRV analysis based on the input associated with HRV include oneor more instructions for performing one or more methods selected fromthe group consisting of strange entropy (StEn), Chaos, correlationdimension, fractal theory, strange attractors, mode entropy (modEn),multifractal, multiscale multifractal, Lyapunov index, base-scaleentropy, and approximate entropy (ApEn).

In some embodiments, the one or more instructions for performingnonlinear HRV analysis based on the input associated with HRV includeone or more instructions for performing HRV analysis using strangeentropy method.

In an illustrative embodiment, the one or more instructions forperforming HRV analysis using strange entropy method include one or moreinstructions for taking a time series signal with N elements,u:{u(i):1≦i≦N}, wherein u(i) represents signals carried in the inputassociated with HRV, and for each u(i), there is a corresponding vectorwith m elements X(i)=[u(i), u(i+1), . . . , u(i+m−1)]; one or moreinstructions for calculating a base scale BS(i) for each vector X(i);one or more instructions for transforming every X(i) to a m-dimensionalsymbol series S_(i)={s(i), . . . s(i+m−1)}, sεA(A=0, 1, 2, 3) based on ascale of a×BS(i); one or more instructions for calculating probabilityof S_(i) such that the probability of each different combination amongthe entire N−m+1 m-dimensional vectors is

${{p(\pi)} = \frac{\#\mspace{14mu}\left\{ t \middle| {\left( {u_{i},\ldots\mspace{14mu},u_{t + m - 1}} \right)\mspace{14mu}{has}\mspace{14mu}{type}\mspace{14mu}\pi} \right\}}{N - m + 1}},$wherein 1≦t≦N−m+1, # is the number of states, and each possiblecombination π for S_(i) represents a vibration mode for S_(i); and oneor more instructions for obtaining one or more of nonlinear HRVparameters or m-word distribution graph according to the probability ofS_(i).

In certain embodiments, u(i) represents R-R interval.

In certain embodiments, BS(i) is defined as square root average ofdifference between adjacent elements

${{BS}(i)} = {\sqrt{\frac{\sum\limits_{j = 1}^{m - 1}\left( \left( {{u\left( {i + j} \right)} - {u\left( {i + j - 1} \right)}} \right)^{2} \right.}{m - 1}}.}$

In certain embodiment, said every X(i) is transformed to a m-dimensionalsymbol series S_(i) according to the transformation equation

$S_{i + k} = \left\{ \begin{matrix}{0\text{:}} & {{\overset{\_}{u}}_{i} < u_{i + k} \leq {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{1\text{:}} & {u_{i + k} > {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{2\text{:}} & {{{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}} < u_{i + k} \leq {\overset{\_}{u}}_{i}} \\{3\text{:}} & {{u_{i + k} \leq {{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}}};}\end{matrix} \right.$wherein i=1, 2, . . . , N−m+1; k=0, 1, . . . m−1; S_(i+k) is the k-thelement of S_(i); u_(i+k) is the k-th element of X(i); ū_(i) representsthe average value of the m-dimensional vector X(i); and 0, 1, 2, 3 arenotation for region partition.

In certain embodiments, said nonlinear HRV parameters include H(m) whichis calculated according to the following equationH(m)=−ΣP(π)log₂ P(π).

In certain embodiments, said nonlinear HRV parameters include PSS whichis defined as sum of probabilities of all strange states calculatedaccording to following equation

${{P\; S\; S} = \frac{\sum P_{s}}{\sum\limits_{i = 0}^{255}P_{i}}};$wherein P_(i) is probability of combination i, P_(s) is probability ofstrange state, and ΣP_(s) is probability sum of all strange states.

In certain embodiments, m is in range of 2-6. In an illustrativeembodiment, m is 4.

In certain embodiments, a is in the range of 0.1-2. In certainembodiments, a is in the range of 0.1-0.4. In an illustrativeembodiment, a is 0.2.

In certain embodiments, N is larger than 4^(m).

In certain embodiments, the lie detection result is obtained byreal-time monitoring using a sliding window. In some embodiments, thesize of the sliding window is in the range of 300-500 data points. Insome embodiments, a sliding step for the sliding window is between 1 and50 data points. In some embodiments, said data points represent heartbeats.

In certain embodiments, the lie detection result is obtained bycomparing the analysis results with normal data.

In certain embodiments, the one or more instructions for performing anHRV analysis based on the input associated with HRV include one or moreinstructions for performing neural network-based linear HRV analysisbased on the input associated with HRV.

In some embodiments, the one or more instructions for performing neuralnetwork-based linear HRV analysis based on the input associated with HRVinclude one or more instructions for acquiring one or more linear HRVparameters from the input associated with HRV; and one or moreinstructions for performing a neural network analysis based on thelinear HRV parameters.

In certain embodiments, the neural network analysis is performed basedon at least three linear HRV parameters.

In certain embodiments, said linear HRV parameters include one or moreof Total Power, Low Frequency Power, High Frequency Power, LFnorm,HFnorm, Low Frequency Power/High Frequency Power, Heart Rate, andStandard Deviation of adjacent R-R Interval.

In certain embodiments, said neural network is trained by an Error BackPropagation algorithm to distinguish lying from non-lying.

In certain embodiments, the one or more instructions for performing HRVanalysis include one or more instructions for performing neuralnetwork-based linear HRV analysis in combination with nonlinear HRVanalysis, wherein the neural network analysis is performed based on acombination of linear HRV parameters and nonlinear HRV parameters.

In some embodiments, the one or more instructions for performing neuralnetwork-based linear HRV analysis in combination with nonlinear HRVanalysis include one or more instructions for acquiring one or morelinear HRV parameters from the input associated with HRV; one or moreinstructions for obtaining one or more nonlinear HRV parameters from theinput associated with HRV; and one or more instructions for performing aneural network analysis based on the linear and nonlinear HRVparameters.

In certain embodiments, said combination of linear HRV parameters andnonlinear HRV parameters includes two nonlinear parameters plus one tosix linear parameters.

In certain embodiments, said linear HRV parameters include one or moreof Total Power, Low Frequency Power, High Frequency Power, LFnorm,HFnorm, Low Frequency Power/High Frequency Power, Heart Rate, andStandard Deviation of adjacent R-R Interval.

In certain embodiments, said nonlinear HRV analysis is performed byusing the strange entropy method as described above, and said nonlinearHRV parameters include H(m) and PSS.

In another aspect, the present disclosure provides a computer readablestorage medium having a computer program encoded thereon, wherein saidcomputer program when executed by a computer instructs the computer toexecute a method of lie detection, which includes receiving an inputassociated with HRV; and performing an HRV analysis based on the inputassociated with HRV to obtain a lie detection result; wherein said HRVanalysis includes one or more of nonlinear HRV analysis, or neuralnetwork-based linear HRV analysis.

In certain embodiments, the computer readable storage medium may be anyof a variety of memory storage devices. Examples of memory storagemedium include any commonly available random access memory (RAM),magnetic medium such as a resident hard disk or tape, an optical mediumsuch as a read and write compact disc, or other memory storage device.

In certain embodiments, the method of lie detection further includesoutputting one or more of the analysis results or lie detection results.

In certain embodiments, said input associated with HRV includes a timeseries of ECG recordings.

In certain embodiments, said HRV analysis includes nonlinear HRVanalysis.

In certain embodiments, said nonlinear HRV analysis is performed byusing one or more methods selected from the group consisting of strangeentropy (StEn), Chaos, correlation dimension, fractal theory, strangeattractors, mode entropy (modEn), multifractal, multiscale multifractal,Lyapunov index, base-scale entropy, and approximate entropy (ApEn).

In certain embodiments, said nonlinear HRV analysis is performed byusing strange entropy method.

In certain embodiment, said strange entropy method includes taking atime series signal with N elements, u:{u(i):1≦i≦N}, wherein u(i)represents signals carried in the input associated with HRV, and foreach u(i), there is a corresponding vector with m elements X(i)=[u(i),u(i+1), . . . , u(i+m−1)]; calculating a base scale BS(i) for eachvector X(i); transforming every X(i) to a m-dimensional symbol seriesS_(i)={s(i), . . . s(i+m−1)},sεA(A=0, 1, 2, 3) based on a scale ofa×BS(i); calculating probability of S_(i) such that the probability ofeach different combination among the entire N−m+1 m-dimensional vectorsis

${{p(\pi)} = \frac{\#\mspace{14mu}\left\{ t \middle| {\left( {u_{i},\ldots\mspace{14mu},u_{t + m - 1}} \right)\mspace{14mu}{has}\mspace{14mu}{type}\mspace{14mu}\pi} \right\}}{N - m + 1}},$wherein 1≦t≦N−m+1, # is the number of states, and each possiblecombination π for S_(i) represents a vibration mode for S_(i); andobtaining one or more of nonlinear HRV parameters or m-word distributiongraph according to the probability of S_(i).

In certain embodiments, u(i) represents R-R interval.

In certain embodiments, BS(i) is defined as square root average ofdifference between adjacent elements

${{BS}(i)} = {\sqrt{\frac{\sum\limits_{j = 1}^{m - 1}\left( \left( {{u\left( {i + j} \right)} - {u\left( {i + j - 1} \right)}} \right)^{2} \right.}{m - 1}}.}$

In certain embodiment, said every X(i) is transformed to a m-dimensionalsymbol series S_(i) according to the transformation equation

$S_{i + k} = \left\{ \begin{matrix}{{0\text{:}}\mspace{11mu}} & {{\overset{\_}{u}}_{i} < u_{i + k} \leq {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{1\text{:}} & {u_{i + k} > {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{2\text{:}} & {{{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}} < u_{i + k} \leq {\overset{\_}{u}}_{i}} \\{3\text{:}} & {{u_{i + k} \leq {{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}}};}\end{matrix} \right.$wherein i=1, 2, . . . , N−m+1; k=0, 1, . . . m−1; S_(i+k) is the k-thelement of S_(i); u_(i+k) is the k-th element of X(i); ū_(i) representsthe average value of the m-dimensional vector X(i); and 0, 1, 2, 3 arenotation for region partition.

In certain embodiments, said nonlinear HRV parameters include H(m) whichis calculated according to the following equationH(m)=−ΣP(π)log₂ P(π).

In certain embodiments, said nonlinear HRV parameters include PSS whichis defined as sum of probabilities of all strange states calculatedaccording to following equation

${{P\; S\; S} = \frac{\sum\limits^{\;}P_{s}}{\sum\limits_{i = 0}^{255}P_{i}}};$wherein P_(i) is probability of combination i, P_(s) is probability ofstrange state, and ΣP_(s) is probability sum of all strange states.

In certain embodiments, m is in range of 2-6. In an illustrativeembodiment, m is 4.

In certain embodiments, a is in the range of 0.1-2. In certainembodiments, a is in the range of 0.1-0.4. In an illustrativeembodiment, a is 0.2.

In certain embodiments, N is larger than 4^(m).

In certain embodiments, the lie detection result is obtained byreal-time monitoring using a sliding window. In some embodiments, thesize of the sliding window is in the range of 300-500 data points. Insome embodiments, a sliding step for the sliding window is between 1 and50 data points. In some embodiments, said data points represent heartbeats.

In certain embodiments, the lie detection result is obtained bycomparing the analysis results with normal data.

In certain embodiments, said HRV analysis includes neural network-basedlinear HRV analysis.

In certain embodiments, said neural network-based linear HRV analysisincludes acquiring one or more linear HRV parameters from the inputassociated with HRV; and performing a neural network analysis based onthe linear HRV parameters.

In certain embodiments, the neural network analysis is performed basedon at least three linear HRV parameters.

In certain embodiments, said linear HRV parameters include one or moreof Total Power, Low Frequency Power, High Frequency Power, LFnorm,HFnorm, Low Frequency Power/High Frequency Power, Heart Rate, andStandard Deviation of adjacent R-R Interval.

In certain embodiments, said neural network is trained by an Error BackPropagation algorithm to distinguish lying from non-lying.

In certain embodiments, said HRV analysis includes neural network-basedlinear HRV analysis in combination with nonlinear HRV analysis, whereinthe neural network analysis is performed based on a combination oflinear HRV parameters and nonlinear HRV parameters.

In certain embodiments, said combination of linear HRV parameters andnonlinear HRV parameters includes two nonlinear parameters plus one tosix linear parameters.

In certain embodiments, said linear HRV parameters include one or moreof Total Power, Low Frequency Power, High Frequency Power, LFnorm,HFnorm, Low Frequency Power/High Frequency Power, Heart Rate, andStandard Deviation of adjacent R-R Interval.

In certain embodiments, said nonlinear HRV analysis is performed byusing the strange entropy method as described above, and said nonlinearHRV parameters include H(m) and PSS.

In another aspect, the present disclosure provides a lie detectionsystem comprising a computing unit configured to receive an inputassociated with HRV; and perform an HRV analysis based on the inputassociated with HRV to obtain a lie detection result; wherein said HRVanalysis includes one or more of nonlinear HRV analysis, or neuralnetwork-based linear HRV analysis.

In certain embodiments, said input associated with HRV includes a timeseries of ECG recordings.

In certain embodiments, said HRV analysis includes nonlinear HRVanalysis.

In certain embodiments, said nonlinear HRV analysis is performed byusing one or more methods selected from the group consisting of strangeentropy (StEn), Chaos, correlation dimension, fractal theory, strangeattractors, mode entropy (modEn), multifractal, multiscale multifractal,Lyapunov index, base-scale entropy, and approximate entropy (ApEn).

In certain embodiments, said nonlinear HRV analysis is performed byusing strange entropy method.

In certain embodiments, the lie detection result is obtained byreal-time monitoring using a sliding window. In some embodiments, thesize of the sliding window is in the range of 300-500 data points. Insome embodiments, a sliding step for the sliding window is between 1 and50 data points. In some embodiments, said data points represent heartbeats.

In certain embodiments, the lie detection result is obtained bycomparing the analysis results with normal data.

In certain embodiments, said HRV analysis includes neural network-basedlinear HRV analysis.

In certain embodiments, said neural network-based linear HRV analysisincludes acquiring one or more linear HRV parameters from the inputassociated with HRV; and performing a neural network analysis based onthe linear HRV parameters.

In certain embodiments, the neural network analysis is performed basedon at least three linear HRV parameters.

In certain embodiments, said linear HRV parameters include one or moreof Total Power, Low Frequency Power, High Frequency Power, LFnorm,HFnorm, Low Frequency Power/High Frequency Power, Heart Rate, andStandard Deviation of adjacent R-R Interval.

In certain embodiments, said neural network is trained by an Error BackPropagation algorithm to distinguish lying from non-lying.

In certain embodiments, said HRV analysis includes neural network-basedlinear HRV analysis in combination with nonlinear HRV analysis, and theneural network analysis is performed based on a combination of linearHRV parameters and nonlinear HRV parameters.

In certain embodiments, said combination of linear HRV parameters andnonlinear HRV parameters includes two nonlinear parameters plus one tosix linear parameters.

In certain embodiments, said linear HRV parameters include one or moreof Total Power, Low Frequency Power, High Frequency Power, LFnorm,HFnorm, Low Frequency Power/High Frequency Power, Heart Rate, andStandard Deviation of adjacent R-R Interval.

In certain embodiments, said nonlinear HRV analysis is performed byusing the strange entropy method as described above, and said nonlinearHRV parameters include H(m) and PSS.

In certain embodiments, said lie detection system further comprises anoutput unit configured to output one or more of the analysis results orlie detection results.

FIG. 5 shows a schematic diagram of an illustrative system 500 in whichembodiments may be implemented. The system 500 may include a computingsystem environment. The system 500 comprises a computing unit 510, andmay optionally comprise one or more of an input unit 520, an output unit530 or a storage medium 540.

In illustrative embodiments, the storage medium 540 is contained inwhole or in part within the computing unit 510. In some illustrativeembodiments, one or more of the input unit 520, the output unit 530 orthe storage medium 540 is in communication with the computing unit 510by way of an optional coupling. The optional coupling may represent alocal, wide area, or peer-to-peer network, or may represent a bus thatis internal to the computing unit 510. In an illustrative embodiment,the computing unit 510 may be integrated with one or more of the inputunit 520, the output unit 530 or the storage medium 540 into one systemunit located at one geographical site. In another illustrativeembodiment, one or more of the computing unit 510, the input unit 520,the output unit 530 or the storage medium 540 may be placed intomultiple separate system units located at a geographical site or severalgeographical sites.

In some illustrative embodiments, the computing unit 510 is configuredto implement one or more of the techniques, processes, or methodsdescribed herein, or other techniques.

In certain embodiments, the computing unit 510 is configured to receivean input associated with HRV; and perform an HRV analysis based on theinput associated with HRV to obtain a lie detection result; wherein saidHRV analysis includes one or more of nonlinear HRV analysis, or neuralnetwork-based linear HRV analysis.

In illustrative embodiments, the computing unit 510 is configured totake a time series signal with N elements, u:{u(i):1≦i≦N}, wherein u(i)represents signals carried in the input associated with HRV, and foreach u(i), there is a corresponding vector with m elements X(i)=[u(i),u(i+1), . . . , u(i+m−1)]; calculate a base scale BS(i) for each vectorX(i); transform every X(i) to a m-dimensional symbol series S_(i)={s(i),. . . s(i+m−1)}, sεA(A=0, 1, 2, 3) based on a scale of a×BS(i);calculate probability of S_(i) such that the probability of eachdifferent combination among the entire N−m+1 m-dimensional vectors is

${{p(\pi)} = \frac{\#\mspace{14mu}\left\{ {t❘{\left( {u_{i},\ldots\mspace{14mu},u_{t + m - 1}} \right)\mspace{14mu}{has}\mspace{14mu}{type}\mspace{14mu}\pi}} \right\}}{N - m + 1}},$wherein 1≦t≦N−m+1, # is the number of states, and each possiblecombination π for S_(i) represents a vibration mode for S_(i); andobtain one or more of nonlinear HRV parameters or m-word distributiongraph according to the probability of S_(i).

In certain embodiments, u(i) represents R-R interval.

In certain embodiments, BS(i) is defined as square root average ofdifference between adjacent elements

${{BS}(i)} = {\sqrt{\frac{\sum\limits_{j = 1}^{m - 1}\left( \left( {{u\left( {i + j} \right)} - {u\left( {i + j - 1} \right)}} \right)^{2} \right.}{m - 1}}.}$

In certain embodiment, said every X(i) is transformed to a m-dimensionalsymbol series S_(i) according to the transformation equation

$S_{i + k} = \left\{ \begin{matrix}{0\text{:}} & {{\overset{\_}{u}}_{i} < u_{i + k} \leq {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{1\text{:}} & {u_{i + k} > {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{2\text{:}} & {{{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}} < u_{i + k} \leq {\overset{\_}{u}}_{i}} \\{3\text{:}} & {{u_{i + k} \leq {{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}}};}\end{matrix} \right.$wherein i=1, 2, . . . , N−m+1; k=0, 1, . . . m−1; S_(i+k) is the k-thelement of S_(i); u_(i+k) is the k-th element of X(i); ū_(i) representsthe average value of the m-dimensional vector X(i); and 0, 1, 2, 3 arenotation for region partition.

In certain embodiments, said nonlinear HRV parameters include H(m) whichis calculated according to the following equationH(m)=−ΣP(π)log₂ P(π).

In certain embodiments, said nonlinear HRV parameters include PSS whichis defined as sum of probabilities of all strange states calculatedaccording to following equation

${{P\; S\; S} = \frac{\sum\limits^{\;}P_{s}}{\sum\limits_{i = 0}^{255}P_{i}}};$wherein P_(i) is probability of combination i, P_(s) is probability ofstrange state, and ΣP_(s) is probability sum of all strange states.

In certain embodiments, m is in range of 2-6. In an illustrativeembodiment, m is 4.

In certain embodiments, a is in the range of 0.1-2. In certainembodiments, a is in the range of 0.1-0.4. In an illustrativeembodiment, a is 0.2.

In certain embodiments, N is larger than 4^(m).

In some illustrative embodiments, the computing unit 510 may include,but is not limit to, one or more of a desktop computer, a workstationcomputer, a computing system comprising a cluster of processors, anetworked computer, a tablet personal computer, a laptop computer, or apersonal digital assistant, or any other suitable computing apparatus.In some embodiments, the computing unit 510 may include one or more of aCPU, a FPGA, a microprocessor, a digital signal processor, an ASIC orany other suitable computing device.

In some illustrative embodiments, the input unit 520 may include, but isnot limit to, one of more of a key board, a USB port, a medical testingequipment, or any other suitable input apparatus operable to input datafor lie detection and/or HRV analysis. The output unit 530 may include,but is not limit to, one or more of a CRT, a LCD, a printer, an audiospeaker or any other suitable output apparatus operable to output one ormore of HRV analysis results or lie detection results in a visual formatand/or an audio format. The storage medium 540 may include, but is notlimited to one or more of a ROM, a RAM, a CD-ROM, a DVD, a tape, a flashmemory, or any other suitable medium operable to store data to beprocessed and/or programs to be executed by the computing unit 510.

In some illustrative embodiments, the medical testing equipmentcontained in the input unit 510 may include, but is not limit to, atester operable to provide data that are able to indicate and/orcalculate HRV, such as an electrocardiograph, a blood pressure trackingtester, a pulse wave tester and the like.

In another aspect, the present disclosure provides a computer programproduct comprising one or more instructions recorded on amachine-readable recording medium for performing HRV analysis usingstrange entropy, wherein the instructions include one or moreinstructions for taking a time series signal with N elements,u:{u(i):1≦i≦N}, wherein u(i) represents signals carried in the inputassociated with HRV, and for each u(i), there is a corresponding vectorwith m elements X(i)=[u(i), u(i+1), . . . , u(i+m−1)]; one orinstructions for calculating a base scale BS(i) for each vector X(i);one or more instructions for transforming every X(i) to a m-dimensionalsymbol series S_(i)={s(i), . . . s(i+m−1)}, sεA(A=0, 1, 2, 3) based on ascale of a×BS(i); one or more instructions for calculating probabilityof S_(i) such that the probability of each different combination amongthe entire N−m+1 m-dimensional vectors is

${{p(\pi)} = \frac{\#\mspace{14mu}\left\{ {t❘{\left( {u_{i},\ldots\mspace{14mu},u_{t + m - 1}} \right)\mspace{14mu}{has}\mspace{14mu}{type}\mspace{14mu}\pi}} \right\}}{N - m + 1}},$wherein 1≦t≦N−m+1, # is the number of states, and each possiblecombination π for S_(i) represents a vibration mode for S_(i); and oneor more instructions for obtaining one or more of nonlinear HRVparameters or m-word distribution graph according to the probability ofS_(i).

In certain embodiments, u(i) represents R-R interval.

In certain embodiments, BS(i) is defined as square root average ofdifference between adjacent elements

${{BS}(i)} = {\sqrt{\frac{\sum\limits_{j = 1}^{m - 1}\left( \left( {{u\left( {i + j} \right)} - {u\left( {i + j - 1} \right)}} \right)^{2} \right.}{m - 1}}.}$

In certain embodiment, said every X(i) is transformed to a m-dimensionalsymbol series S_(i) according to the transformation equation

$S_{i + k} = \left\{ \begin{matrix}{0\text{:}} & {{\overset{\_}{u}}_{i} < u_{i + k} \leq {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{1\text{:}} & {u_{i + k} > {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{2\text{:}} & {{{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}} < u_{i + k} \leq {\overset{\_}{u}}_{i}} \\{3\text{:}} & {{u_{i + k} \leq {{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}}};}\end{matrix} \right.$wherein i=1, 2, . . . , N−m+1; k=0, 1, . . . m−1; S_(i+k) is the k-thelement of S_(i); u_(i+k) is the k-th element of X(i); ū_(i) representsthe average value of the m-dimensional vector X(i); and 0, 1, 2, 3 arenotation for region partition.

In certain embodiments, said nonlinear HRV parameters include H(m) whichis calculated according to the following equationH(m)=−ΣP(π)log₂ P(π).

In certain embodiments, said nonlinear HRV parameters include PSS whichis defined as sum of probabilities of all strange states calculatedaccording to following equation

${{P\; S\; S} = \frac{\sum\limits^{\;}P_{s}}{\sum\limits_{i = 0}^{255}P_{i}}};$wherein P_(i) is probability of combination i, P_(s) is probability ofstrange state, and ΣP_(s) is probability sum of all strange states.

In certain embodiments, m is in range of 2-6. In an illustrativeembodiment, m is 4.

In certain embodiments, a is in the range of 0.1-2. In certainembodiments, a is in the range of 0.1-0.4. In an illustrativeembodiment, a is 0.2.

In certain embodiments, N is larger than 4^(m).

A person with ordinary skill in the art will appreciate that a computerprogram product described herein are capable of being distributed in avariety of forms via a signal bearing medium, and that the programproduct described herein applies regardless of the particular type ofsignal bearing medium used to actually carry out the distribution.Examples of a signal bearing medium include, but are not limited to, arecordable type medium such as a floppy disk, a hard disk drive, aCompact Disc (CD), a Digital Video Disk (DVD), a digital tape, acomputer memory, etc.; and a transmission type medium such as a digitaland/or an analog communication medium (e.g., a fiber optic cable, awaveguide, a wired communications link, a wireless communication link,etc.).

The machine-readable storage media mentioned above include, but notlimited to various memories and storage units, a semiconductor device, adisk unit such as an optic disk, a magnetic disk and a magneto-opticdisk, and other media applicable to information storage. It is alsopossible to execute the program on a client computer after computerprogram codes are downloaded and installed on the computer from acorresponding internet website connected with the computer.

In another aspect, the present disclosure provides a computer readablestorage medium having a computer program encoded thereon, wherein saidcomputer program when executed by a computer instructs the computer toperform HRV analysis using strange entropy, which includes taking a timeseries signal with N elements, u:{u(i):1≦i≦N}, wherein u(i) representssignals carried in the input associated with HRV, and for each u(i),there is a corresponding vector with m elements X(i)=[u(i), u(i+1), . .. , u(i+m−1)]; calculating a base scale BS(i) for each vector X(i);transforming every X(i) to a m-dimensional symbol series S_(i)={s(i), .. . s(i+m−1)}, sεA(A=0, 1, 2, 3) based on a scale of a×BS(i);calculating the probability of S_(i), wherein the probability of eachdifferent combination among the entire N−m+1 m-dimensional vectors is

${{p(\pi)} = \frac{\#\mspace{14mu}\left\{ {t❘{\left( {u_{i},\ldots\mspace{14mu},u_{t + m - 1}} \right)\mspace{14mu}{has}\mspace{14mu}{type}\mspace{14mu}\pi}} \right\}}{N - m + 1}},$wherein 1≦t≦N−m+1; # is the number of states, and each possiblecombination π for S_(i) represents a vibration mode for S_(i); andobtaining one or more of nonlinear HRV parameters or m-word distributiongraph according to the probability of S_(i).

In certain embodiments, u(i) represents R-R interval.

In certain embodiments, BS(i) is defined as square root average ofdifference between adjacent elements

${{BS}(i)} = {\sqrt{\frac{\sum\limits_{j = 1}^{m - 1}\left( \left( {{u\left( {i + j} \right)} - {u\left( {i + j - 1} \right)}} \right)^{2} \right.}{m - 1}}.}$

In certain embodiment, said every X(i) is transformed to a m-dimensionalsymbol series S_(i) according to the transformation equation

$S_{i + k} = \left\{ \begin{matrix}{0\text{:}} & {{\overset{\_}{u}}_{i} < u_{i + k} \leq {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{1\text{:}} & {u_{i + k} > {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{2\text{:}} & {{{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}} < u_{i + k} \leq {\overset{\_}{u}}_{i}} \\{3\text{:}} & {{u_{i + k} \leq {{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}}};}\end{matrix} \right.$wherein i=1, 2, . . . , N−m+1; k=0, 1, . . . m−1; S_(i+k) is the k-thelement of S_(i); u_(i+k) is the k-th element of X(i); ū_(i) representsthe average value of the m-dimensional vector X(i); and 0, 1, 2, 3 arenotation for region partition.

In certain embodiments, said nonlinear HRV parameters include H(m) whichis calculated according to the following equationH(m)=−ΣP(π)log₂ P(π).

In certain embodiments, said nonlinear HRV parameters include PSS whichis defined as sum of probabilities of all strange states calculatedaccording to following equation

${{P\; S\; S} = \frac{\sum P_{s}}{\sum\limits_{i = 0}^{255}P_{i}}};$wherein P_(i) is probability of combination i, P_(s) is probability ofstrange state, and ΣP_(s) is probability sum of all strange states.

In certain embodiments, m is in range of 2-6. In an illustrativeembodiment, m is 4.

In certain embodiments, a is in the range of 0.1-2. In certainembodiments, a is in the range of 0.1-0.4. In an illustrativeembodiment, a is 0.2.

In certain embodiments, N is larger than 4^(m).

In certain embodiments, the computer readable storage medium may be anyof a variety of memory storage devices. Examples of memory storagemedium include any commonly available random access memory (RAM),magnetic medium such as a resident hard disk or tape, an optical mediumsuch as a read and write compact disc, or other memory storage device.

In another aspect, the present disclosure provides an HRV analysissystem, comprising a computing unit configured to take a time seriessignal with N elements, u:{u(i):1≦i≦N}, wherein u(i) represents signalscarried in the input associated with HRV, and for each u(i), there is acorresponding vector with m elements X(i)=[u(i), u(i+1, u(i+m−1)];calculate a base scale BS(i) for each vector X(i); transform every X(i)to a m-dimensional symbol series S_(i)={s(i), . . . s(i+m−1)}, sεA(A=0,1, 2, 3) based on a scale of a×BS(i); calculate the probability ofS_(i), wherein the probability of each different combination among theentire N−m+1 m-dimensional vectors is

${{p(\pi)} = \frac{\#\mspace{14mu}\left\{ t \middle| {\left( {u_{i},\ldots\mspace{11mu},u_{t + m - 1}} \right)\mspace{14mu}{has}\mspace{14mu}{type}\mspace{14mu}\pi} \right\}}{N - m + 1}},$wherein 1≦t≦N−m+1; # is the number of states, and each possiblecombination π for S_(i) represents a vibration mode for S_(i); andobtain one or more of nonlinear HRV parameters or m-word distributiongraph according to the probability of S_(i).

In certain embodiments, u(i) represents R-R interval.

In certain embodiments, BS(i) is defined as square root average ofdifference between adjacent elements

${{BS}(i)} = {\sqrt{\frac{\sum\limits_{j = 1}^{m - 1}\left( \left( {{u\left( {i + j} \right)} - {u\left( {i + j - 1} \right)}} \right)^{2} \right.}{m - 1}}.}$

In certain embodiment, said every X(i) is transformed to a m-dimensionalsymbol series S_(i) according to the transformation equation

$S_{i + k} = \left\{ \begin{matrix}{0\text{:}} & {{\overset{\_}{u}}_{i} < u_{i + k} \leq {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{1\text{:}} & {u_{i + k} > {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{2\text{:}} & {{{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}} < u_{i + k} \leq {\overset{\_}{u}}_{i}} \\{3\text{:}} & {{u_{i + k} \leq {{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}}};}\end{matrix} \right.$wherein i=1, 2, . . . , N−m+1; k=0, 1, . . . m−1; S_(i+k) is the k-thelement of S_(i); u_(i+k) is the k-th element of X(i); ū_(i) representsthe average value of the m-dimensional vector X(i); and 0, 1, 2, 3 arenotation for region partition.

In certain embodiments, said nonlinear HRV parameters include H(m) whichis calculated according to the following equationH(m)=−ΣP(π)log₂ P(π).

In certain embodiments, said nonlinear HRV parameters include PSS whichis defined as sum of probabilities of all strange states calculatedaccording to following equation

${{P\; S\; S} = \frac{\sum P_{s}}{\sum\limits_{i = 0}^{255}P_{i}}};$wherein P_(i) is probability of combination i, P_(s) is probability ofstrange state, and ΣP_(s) is probability sum of all strange states.

In certain embodiments, m is in range of 2-6. In an illustrativeembodiment, m is 4.

In certain embodiments, a is in the range of 0.1-2. In certainembodiments, a is in the range of 0.1-0.4. In an illustrativeembodiment, a is 0.2.

In certain embodiments, N is larger than 4^(m).

In certain embodiments, the HRV analysis system further comprises one ormore of an input unit, an output unit or a storage medium.

There is little distinction left between hardware and softwareimplementations of aspects of systems; the use of hardware or softwareis generally (but not always, in that in certain contexts the choicebetween hardware and software can become significant) a design choicerepresenting cost vs. efficiency tradeoffs. There are various vehiclesby which processes and/or systems and/or other technologies describedherein can be effected (e.g., hardware, software, and/or firmware), andthat the preferred vehicle will vary with the context in which theprocesses and/or systems and/or other technologies are deployed. Forexample, if an implementer determines that speed and accuracy areparamount, the implementer may opt for a mainly hardware and/or firmwarevehicle; if flexibility is paramount, the implementer may opt for amainly software implementation; or, yet again alternatively, theimplementer may opt for some combination of hardware, software, and/orfirmware.

Those skilled in the art will recognize that it is common within the artto describe devices and/or processes in the fashion set forth herein,and thereafter use engineering practices to integrate such describeddevices and/or processes into data processing systems. That is, at leasta portion of the devices and/or processes described herein can beintegrated into a data processing system via a reasonable amount ofexperimentation. Those having skill in the art will recognize that atypical data processing system generally includes one or more of asystem unit housing, a video display device, a memory such as volatileand non-volatile memory, processors such as microprocessors and digitalsignal processors, computational entities such as operating systems,drivers, graphical user interfaces, and applications programs, one ormore interaction devices, such as a touch pad or screen, and/or controlsystems including feedback loops and control motors (e.g., feedback forsensing position and/or velocity; control motors for moving and/oradjusting components and/or quantities). A typical data processingsystem may be implemented utilizing any suitable commercially availablecomponents, such as those typically found in datacomputing/communication and/or network computing/communication systems.

EXAMPLES

The following Examples are set forth to aid in the understanding of thepresent disclosure, and should not be construed to limit in any way thescope of the technology as defined in the claims which followthereafter.

Example 1

This Example shows an illustrative embodiment of a method of HRVanalysis using strange entropy and how it may be used for lie detection.The strange entropy HRV analysis was performed on a young adult, an oldadult and a CHF patient, respectively, following the operationsdescribed in the Detailed Description above, with symbol number as {0,1, 2, 3}, dimension m=4 and parameter a=0.2.

As shown in FIG. 6, a histogram of m-word distribution, largestamplitudes are observed at horizontal value 95, 125, 215, 245, when theHRV analysis using strange entropy was performed on a young adult. Usingthe language of m-words, those four strange states of the system arerespectively 1133, 1331, 3113 and 3311, which are healthy combinationstates. The same analysis was performed on an old healthy adult, and asshown in FIG. 7, largest amplitudes are also found at horizontal value95, 125, 215, 245.

Comparing FIGS. 6 and 7, it is found that the number of the forbiddenstates in FIG. 6 is larger than that in FIG. 7, showing that physicalfunction of human body degrades with age, since the number of theforbidden states represents health level in a certain extent. However,the four healthy combination states, which have the highest probability,remain the same, i.e. 1133, 1331, 3113 and 3311, respectively, althoughthe probability of each healthy combination state in FIG. 7 is smallerthan that of the corresponding healthy combination state in FIG. 6.

In FIG. 8, the test subject was a chronic heart failure (CHF) patientwhile other test parameters used in the analysis were the same. It isfound that largest amplitudes are not at horizontal value 95, 125, 215,245.

As the comparison between FIGS. 6, 7 and 8 indicates, the four healthycombination states, which are respectively 1133, 1331, 3113 and 3311,have the highest probability and are not relevant to any one orcombination of factors such as age, sex, and body shape, whereas personswith heart diseases such as chronic heart failure may presentsignificant variations in the distribution pattern. It can thus bereasonably expected that when a person is under abnormal psychologicalconditions, such as lying, the distribution pattern will likewise becomeirregular.

Example 2

Using the same methodology as in Example 1, an HRV analysis wasperformed on a group of young adults, a group of old adults and a groupof CHF patients, respectively FIG. 9 and FIG. 10 show the strangeentropy values and the PSS values obtained, respectively (shown asmean±SD).

As can be seen from FIG. 9, the strange entropy value of CHF patients ishigher than those of the other two groups of test subjects, the strangeentropy value of old adults is higher than that of young adults, and thedifferences between the strange entropy values of any two groups of testsubjects are obvious to be observed and distinguished. As the value ofthe strange entropy indicates complexity in time series, as well as theself-adjusting ability of parasympathetic system, it increases withdisease and aging.

As can be seen from FIG. 10, the total probability PSS of young adultshas the highest value and the total probability PSS of CHF patients hasthe lowest value. The differences between the PSS values of any twogroups of test subjects are big enough to be distinguished. The totalprobability of the strange status PSS increases with decreasedsympathetic activity, which occurs when a person is getting older, hasdegraded physical functions or diseases, or is disturbed by any abnormalpsychological conditions, such as lying.

As indicated, the strange entropy value represents complexity in timeseries, and the PSS value indicates healthy vibration mode of the timeseries. The more disorder of the healthy vibration mode, the highercomplex in time series, and the total probability PSS of the occurrenceof strange status decreases, the value of strange entropy increases,accordingly.

Based on the results shown in Example 1 and Example 2, the results ofthe nonlinear HRV analysis using strange entropy, including m-worddistribution, H(m) and PSS values of the test subject, may be comparedwith normal data, e.g. obtained from a normal person or a group ofnormal persons, and if there is an irregular or abnormal pattern or adeviation from normal range, it can be determined that the testingsubject is lying on the question being asked.

Example 3

This Example shows an illustrative embodiment of a method of liedetection as described herein, using strange entropy nonlinear HRVanalysis, by comparing the analysis results obtained from a test subject(a young adult) under anxious condition with those obtained from thesame test subject under normal state. The total test lasted about 40minutes.

FIG. 11 shows a histogram of m-word distribution when the test subjectwas calm, and FIG. 12 shows a histogram of m-word distribution when theyoung adult became anxious and uneasy. It is clear that distributionpatterns are significantly different between these two psychologicalstates.

In addition, the values of the total probability PSS and strange entropyH(m) of the young adult under these two conditions were calculated,shown as “PS” and “Entropy”, respectively, beneath the histograms. Whenthe young adult became anxious, i.e. sympathetic activity increased, thetotal probability PSS of the occurrence of strange status decreased andthe value of strange entropy H(m) increased.

In conclusion, this Example clearly shows that the HRV analysis providedin the present disclosures can sensitively and accurately detectabnormal psychological conditions and thus can be used for liedetection.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or inter-medial components. Likewise, any two componentsso associated can also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable”, to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

What is claimed is:
 1. A method of lie detection, comprising: receivingan input associated with heart rate variability “HRV”; and performing anHRV analysis based on the input associated with HRV to obtain a liedetection result; wherein said HRV analysis includes nonlinear HRVanalysis, wherein the nonlinear HRV analysis includes the following:taking a time series with N elements, u :{u(i): 1 ≦i≦N}, wherein u(i)represents signals carried in the input associated with HRV, and foreach u(i), there is a corresponding vector with m elements:X(i)=[u(i),u(i+1), . . . , u(i+m−1)]; calculating a base scale BS(i) foreach vector X(i); transforming every X(i) to a m-dimensional symbolseries S_(i)={s(i), . . . s(i+m−1)},sεA(A=0, 1, 2, 3) based on a scaleof a×BS(i); calculating the probability of S_(i), wherein theprobability of each different combination among the entire N−m+1m-dimensional vectors is:${{p(\pi)} = \frac{\#\mspace{14mu}\left\{ t \middle| {\left( {u_{i},\ldots\mspace{11mu},u_{t + m - 1}} \right)\mspace{14mu}{has}\mspace{14mu}{type}\mspace{14mu}\pi} \right\}}{N - m + 1}},$wherein 1 ≦t ≦N−m+1; # is the number of states, and each possiblecombination π for S_(i) represents a vibration mode for S_(i); obtainingnonlinear HRV parameters or m-word distribution graph according to theprobability of S_(i); and obtaining a nonlinear HRV parameter PSS, whichis defined as sum probabilities of all strange states calculatedaccording to the following equation:${{P\; S\; S} = \frac{\sum P_{s}}{\sum\limits_{i = 0}^{255}P_{i}}};$wherein P_(i) is probability of combination i, P_(s) is probability ofstrange state, and ΣP_(s) is probability sum of all strange states.
 2. Alie detection system, comprising: a computing unit configured to receivean input associated with HRV; and perform an HRV analysis based on theinput associated with HRV to obtain a lie detection result; wherein saidHRV analysis includes nonlinear HRV analysis, wherein the nonlinear HRVanalysis includes the following: obtaining a nonlinear HRV parameterPSS, which is defined as sum probabilities of all strange statescalculated according to the following equation:${{P\; S\; S} = \frac{\sum P_{s}}{\sum\limits_{i = 0}^{255}P_{i}}};$wherein P_(i) is probability of combination i, P_(s) is probability ofstrange state, and ΣP_(s) is probability sum of all strange states. 3.The lie detection system according to claim 2, wherein said nonlinearHRV analysis is performed by using strange entropy method comprising:taking a time series signal with N elements, u :{u(i): 1 ≦i≦N} ,whereinu(i) represents signals carried in the input associated with HRV, andfor each u(i), there is a corresponding vector with m elements:X(i)=[u(i),u(i+1), . . . , u(i+m−1)]; calculating a base scale BS(i) foreach vector X(i); transforming every X(i) to a m-dimensional symbolseries S_(i)={s(i), . . . s(i+m−1)},sεA(A=0, 1, 2, 3) based on a scaleof a×BS(i); calculating probability of S_(i) such that the probabilityof each different combination among the entire N−m +1 m-dimensionalvectors is:${{p(\pi)} = \frac{\#\mspace{14mu}\left\{ t \middle| {\left( {u_{i},\ldots\mspace{11mu},u_{t + m - 1}} \right)\mspace{14mu}{has}\mspace{14mu}{type}\mspace{14mu}\pi} \right\}}{N - m + 1}},$wherein 1 ≦t ≦N −m+1 ,# is the number of states, and each possiblecombination π for S_(i) represents a vibration mode for S_(i); andobtaining one or more of nonlinear HRV parameters or m-word distributiongraph according to the probability of S_(i).
 4. A method of liedetection, comprising: receiving an input associated with heart ratevariability “HRV”; and performing an HRV analysis based on the inputassociated with HRV to obtain a lie detection result; wherein said HRVanalysis includes one or more of nonlinear HRV analysis, or neuralnetwork-based linear HRV analysis, wherein said nonlinear HRV analysisis performed by using one or more methods selected from the groupconsisting of strange entropy (StEn), Chaos, correlation dimension,fractal theory, strange attractors, mode entropy (modEn), multifractal,multiscale multifractal, Lyapunov index, base-scale entropy, andapproximate entropy (ApEn).
 5. The method of lie detection according toclaim 4, wherein said input associated with HRV includes a time seriesof ECG recordings.
 6. The method of lie detection according to claim 4,wherein said HRV analysis is nonlinear HRV analysis.
 7. The method oflie detection according to claim 6, wherein said nonlinear HRV analysisis performed by using one or more methods selected from the groupconsisting of strange entropy (StEn) and base-scale entropy.
 8. Themethod of lie detection according to claim 7, wherein said nonlinear HRVanalysis is performed by: taking a time series signal with N elements,u:{u(i):1≦i≦N}, wherein u(i) represents signals carried in the inputassociated with HRV, and for each u(i), there is a corresponding vectorwith m elements:X(i)=[u(i),u(i+1), . . . , u(i+m−1)]; calculating a base scale BS(i) foreach vector X(i); transforming every X(i) to a m-dimensional symbolseries S_(i)={s(i), . . . s(i+m−1)},sεA(A=0, 1, 2, 3) based on a scaleof a×BS(i); calculating probability of S_(i) such that the probabilityof each different combination among the entire N−m+1m-dimensionalvectors is:${{p(\pi)} = \frac{\#\mspace{14mu}\left\{ t \middle| {\left( {u_{i},\ldots\mspace{11mu},u_{t + m - 1}} \right)\mspace{14mu}{has}\mspace{14mu}{type}\mspace{14mu}\pi} \right\}}{N - m + 1}},$wherein 1≦t≦N−m+1,# is the number of states, and each possiblecombination π for S_(i) represents a vibration mode for S_(i); andobtaining one or more of nonlinear HRV parameters or m-word distributiongraph according to the probability of S_(i).
 9. The method of liedetection according to claim 8, wherein u(i) represents R-R interval.10. The method of lie detection according to claim 8, wherein BS(i) isdefined as square root average of difference between adjacent elements:${{BS}(i)} = {\sqrt{\frac{\sum\limits_{j = 1}^{m - 1}\left( \left( {{u\left( {i + j} \right)} - {u\left( {i + j - 1} \right)}} \right)^{2} \right.}{m - 1}}.}$11. The method of lie detection according to claim 8, wherein said everyX(i) is transformed to a m-dimensional symbol series S_(i) according tothe transformation equation: $S_{i + k} = \left\{ \begin{matrix}{0\text{:}} & {{\overset{\_}{u}}_{i} < u_{i + k} \leq {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{1\text{:}} & {u_{i + k} > {{\overset{\_}{u}}_{i} + {a \times {{BS}(i)}}}} \\{2\text{:}} & {{{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}} < u_{i + k} \leq {\overset{\_}{u}}_{i}} \\{3\text{:}} & {{u_{i + k} \leq {{\overset{\_}{u}}_{i} - {a \times {{BS}(i)}}}};}\end{matrix} \right.$ wherein i =1, 2, . . . , N−m+1; k =0, 1, . . .m−1; S_(i+k) is the k-th element of S_(i); u_(i+k) is the k-th elementof X (i); ū_(i) represents the average value of the m-dimensional vectorX (i); and 0, 1, 2, 3 are notation for region partition.
 12. The methodof lie detection according to claim 8, wherein said nonlinear HRVparameters include H(m) which is calculated according to the followingequation:H(m)=−ΣP(π)log₂ P(π).
 13. The method of lie detection according to claim8, wherein said nonlinear HRV parameters include PSS which is defined assum of probabilities of all strange states calculated according tofollowing equation:${{P\; S\; S} = \frac{\sum P_{s}}{\sum\limits_{i = 0}^{255}P_{i}}};$wherein P_(i) is probability of combination i, P_(s) is probability ofstrange state, and ΣP_(s) is probability sum of all strange states. 14.The method of lie detection according to claim 8, wherein m is 4, a is0.2 and N is larger than 4^(m).
 15. The method of lie detectionaccording to claim 8, wherein the lie detection result is obtained byreal-time monitoring using a sliding window.
 16. The method of liedetection according to claim 8, wherein the lie detection result isobtained by comparing the analysis results with normal data.
 17. Themethod of lie detection according to claim 4, wherein said HRV analysisincludes neural network-based linear HRV analysis comprising: acquiringone or more linear HRV parameters from the input associated with HRV;and performing a neural network analysis based on the linear HRVparameters.
 18. The method of lie detection according to claim 17,wherein the neural network analysis is performed based on at least threelinear HRV parameters selected from the group consisting of Total Power,Low Frequency Power, High Frequency Power, LFnorm, HFnorm, Low FrequencyPower/High Frequency Power, Heart Rate, and Standard Deviation ofadjacent R-R Interval.
 19. The method of lie detection according toclaim 4, wherein said HRV analysis includes neural network-based linearHRV analysis in combination with nonlinear HRV analysis, and wherein theneural network analysis is performed based on a combination of linearHRV parameters and nonlinear HRV parameters.
 20. The method of liedetection according to claim 19, wherein said combination of linear HRVparameters and nonlinear HRV parameters includes two nonlinear HRVparameters H(m) and PSS and one to six linear HRV parameters selectedfrom the group consisting of Total Power, Low Frequency Power, HighFrequency Power, LFnorm, HFnorm, Low Frequency Power/High FrequencyPower, Heart Rate, and Standard Deviation of adjacent R-R Interval. 21.The method of lie detection according to claim 4, further comprisingoutputting one or more of the analysis results or lie detection results.